BiAIT*: Symmetrical Bidirectional Optimal Path Planning With Adaptive Heuristic

Abstract

Adaptively Informed Trees (AIT*) is an algorithm that uses the problem-specific heuristic to avoid unnecessary searches, which significantly improves its performance, especially when collision checking is expensive. However, the heuristic estimation in AIT* consumes lots of computational resources, and its asymmetric bidirectional searching strategy cannot fully exploit the potential of the bidirectional method. In this article, we propose an extension of AIT* called BiAIT*. Unlike AIT*, BiAIT* uses symmetrical bidirectional search for both the heuristic and space searching. The proposed method allows BiAIT* to find the initial solution faster than AIT*, and update the heuristic with less computation when a collision occurs. We evaluated the performance of BiAIT* through simulations and experiments, and the results show that BiAIT* can find the solution faster than state-of-the-art methods. We also analyze the reasons for the different performances between BiAIT* and AIT*. Furthermore, we discuss two simple but effective modifications to fully exploit the potential of the adaptively heuristic method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is inspired by the adaptively heuristic method and the symmetrical bidirectional searching method. The article introduces a novel algorithm that uses the symmetrical bidirectional method to calculate the adaptive heuristic and efficiently search the state space. The problem-specific heuristic in BiAIT* is derived from a lazy-forward tree and a lazy-reverse tree, which are constructed without collision checking. The lazy-forward and lazy-reverse trees are enabled to meet in the middle, thus generating the effective and accurate heuristic. In BiAIT*, the lazy-forward and lazy-reverse trees share heuristic information and jointly guide the growth of the forward and reverse trees, which conduct collision checking and guarantee the feasibility of their edges. Compared with state-of-the-art methods, BiAIT* finds the initial heuristic and updates the heuristic more quickly. The proposed algorithm can be applied to industrial robots, medical robots, or service robots to achieve efficient path planning. The implementation of BiAIT* is available at https://github.com/Licmjy-CU/BiAITstar.